A case study on machine learning model for code review expert system in software engineering

Madera, Michał; Tomon, Rafal

doi:10.15439/2017f536

Cited by 12 publications

(7 citation statements)

References 10 publications

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“…Pradhan et al use machine learning to predict software defects in large systems [95]. Madera and Tomon apply machine learning to identify source code artefacts that are probably endangered by software defects [96]. Software defect prediction is highly influenced by the amount of availability of data for training the machine learning models, including neural networks, SVM, KNN, K-Means Clustering, Naive Bayes, decision trees, logistic and linear regression models, as well as their combinations with ensemble learning [97].…”

Section: Adaptive Methods In Quality Modellingmentioning

confidence: 99%

Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality

2021

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Execution tracing is a tool used in the course of software development and software maintenance to identify the internal routes of execution and state changes while the software operates. Its quality has a high influence on the duration of the analysis required to locate software faults. Nevertheless, execution tracing quality has not been described by a quality model, which is an impediment while measuring software product quality. In addition, such a model needs to consider uncertainty, as the underlying factors involve human analysis and assessment. The goal of this study is to address both issues and to fill the gap by defining a quality model for execution tracing. The data collection was conducted on a defined study population with the inclusion of software professionals to consider their accumulated experiences; moreover, the data were processed by genetic algorithms to identify the linguistic rules of a fuzzy inference system. The linguistic rules constitute a human-interpretable rule set that offers further insights into the problem domain. The study found that the quality properties accuracy, design and implementation have the strongest impact on the quality of execution tracing, while the property legibility is necessary but not completely inevitable. Furthermore, the quality property security shows adverse effects on the quality of execution tracing, but its presence is required to some extent to avoid leaking information and to satisfy legal expectations. The created model is able to describe execution tracing quality appropriately. In future work, the researchers plan to link the constructed quality model to overall software product quality frameworks to consider execution tracing quality with regard to software product quality as a whole. In addition, the simplification of the mathematically complex model is also planned to ensure an easy-to-tailor approach to specific application domains.

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Section: Adaptive Methods In Quality Modellingmentioning

confidence: 99%

Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality

2021

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“…Moreover, imbalanced sizes of software projects and datasets were also pointed out to be major obstacles in evaluating the techniques empirically [70,207]. Lack of generalizability and overfitting problems appeared to be the highest limitation in the articles as the ML models have shown fewer results when applied to diverse cross-project datasets [122,144]. Future directions include improvement of precision while maintaining recall in ML models [70].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning for Software Engineering: A Systematic Mapping

Shafiq,

Mashkoor,

Mayr-Dorn

et al. 2020

Preprint

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The software development industry is rapidly adopting machine learning for transitioning modern day software systems towards highly intelligent and self-learning systems. However, the full potential of machine learning for improving the software engineering life cycle itself is yet to be discovered, i.e., up to what extent machine learning can help reducing the effort/complexity of software engineering and improving the quality of resulting software systems. To date, no comprehensive study exists that explores the current state-of-the-art on the adoption of machine learning across software engineering life cycle stages. Objective: This article addresses the aforementioned problem and aims to present a state-of-the-art on the growing number of uses of machine learning in software engineering. Method: We conduct a systematic mapping study on applications of machine learning to software engineering following the standard guidelines and principles of empirical software engineering.Results: This study introduces a machine learning for software engineering (MLSE) taxonomy classifying the state-of-the-art machine learning techniques according to their applicability to various software engineering life cycle stages. Overall, 227 articles were rigorously selected and analyzed as a result of this study. Conclusion: From the selected articles, we explore a variety of aspects that should be helpful to academics and practitioners alike in understanding the potential of adopting machine learning techniques during software engineering projects.

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“…As observed in some studies, e.g., [140,176], the lack of generalizability regarded as over-fitting problems is one of few major limiting factors, which decreases the accuracy of results. This leads to lesser results when ML models are applied to diverse cross-project datasets.…”

Section: B Limitationsmentioning

confidence: 99%

A Literature Review of Using Machine Learning in Software Development Life Cycle Stages

Shafiq¹,

Mashkoor

Mayr-Dorn

et al. 2021

IEEE Access

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The software engineering community is rapidly adopting machine learning for transitioning modern-day software towards highly intelligent and self-learning systems. However, the software engineering community is still discovering new ways how machine learning can offer help for various software development life cycle stages. In this article, we present a study on the use of machine learning across various software development life cycle stages. The overall aim of this article is to investigate the relationship between software development life cycle stages, and machine learning tools, techniques, and types. We attempt a holistic investigation in part to answer the question of whether machine learning favors certain stages and/or certain techniques.

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A case study on machine learning model for code review expert system in software engineering

Cited by 12 publications

References 10 publications

Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality

Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality

Machine Learning for Software Engineering: A Systematic Mapping

A Literature Review of Using Machine Learning in Software Development Life Cycle Stages

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